The learning resources hub is a central place for clinicians, investigators, community members, funders and ethics committee members to access educational materials about adaptive clinical trials and resources on Bayesian Network models.
For access to these resources, please click on the links below:
Bayesian network models
Good decision-making is difficult in many healthcare settings, because of the complex interplay between risks and benefits at both individual and community levels. There is a need for tools that enable good decisions, particularly for problems such as inappropriate antibiotic prescriptions. Bayesian network modelling is a promising approach to these problems as it helps to organise complex information under a causal inference framework by integrating data (evidence) with subject-matter knowledge provided by domain experts.
After several years of collaborating with experts in Bayesian network and clinicians, the Adaptive Health Intelligence team has piloted the use of these models to help decision making for a range of problems. These include: (1) identifying which bacteria are causing bone infections in children (2) identifying which antibiotic would be most effective to treat urinary tract infections in children (3) increasing our understanding of exacerbations for patients with cystic fibrosis, and (4) furthering our knowledge as to why some children are not fully vaccinated.
Our aim is to ensure these models can be effectively translated as implementable treatment recommendations, and these decision-support tools can be seamlessly incorporated into clinical workflows. We anticipate that our ongoing work on informing decisions in antibiotic prescribing will be influential in promoting a shift in clinical practice from generic (one-size-fits-all) to individually tailored ‘personalised’ treatment recommendations
To learn more about BN models, you can download the information pack below or visit our research partners at Bayesian Intelligence who have developed more information resources on their website.
Bayesian information pack
Bayesian Intelligence website